Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts the Attention Depending on Task Requirements

Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori, Shuki Goto, Tetsuya Ogata

研究成果: Conference contribution

抜粋

For an autonomous robot to flexibly move in response to various tasks or environmental changes, an attention mechanism is required that is based on the robot's behavioral experience. In this paper, we visualize how attention is acquired inside a neural network learned using supervised learning and describe how to acquire a suitable representation for performing a task. Our experimental evaluation shows that the attention was automatically acquired for objects that are needed to perform tasks by learning the time-series of both vision and motor information rather than only vision information. By multimodal learning, the attention is robust against unlearned conditions which background changes or obstacles.

元の言語English
ホスト出版物のタイトルProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
出版者Institute of Electrical and Electronics Engineers Inc.
ページ188-194
ページ数7
ISBN(電子版)9781728166674
DOI
出版物ステータスPublished - 2020 1
イベント2020 IEEE/SICE International Symposium on System Integration, SII 2020 - Honolulu, United States
継続期間: 2020 1 122020 1 15

出版物シリーズ

名前Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

Conference

Conference2020 IEEE/SICE International Symposium on System Integration, SII 2020
United States
Honolulu
期間20/1/1220/1/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

フィンガープリント Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts the Attention Depending on Task Requirements' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Ito, H., Yamamoto, K., Mori, H., Goto, S., & Ogata, T. (2020). Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts the Attention Depending on Task Requirements. : Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020 (pp. 188-194). [9026205] (Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SII46433.2020.9026205